263-5051-00L AI Center Projects in Machine Learning Research
| Semester | Spring Semester 2024 |
| Lecturers | A. Ilic, E. Bamas, N. Davoudi, D. M. A. Duroux, F. Engelmann, S. Gashi, B. Moseley, X. Shen, F. Shi, X. Zhang |
| Periodicity | yearly recurring course |
| Language of instruction | English |
| Comment | Last cancellation/deregistration date for this ungraded semester performance: Friday, 15 March 2024! Please note that after that date no deregistration will be accepted and the course will be considered as "fail". |
| Abstract | The course will give students an overview of selected topics in advanced machine learning that are currently subjects of active research. The course concludes with a final project. | |||||||||||||||||||||
| Learning objective | The overall objective is to give students a concrete idea of what working in contemporary machine learning research is like and inform them about current research performed at ETH. In this course, students will be able to get an overview of current research topics in different specialized areas. In the final project, students will be able to build experience in practical aspects of machine learning research, including research literature, aspects of implementation, and reproducibility challenges. | |||||||||||||||||||||
| Content | The course will be structured as sections taught by different postdocs specialized in the relevant fields. Each section will showcase an advanced research topic in machine learning, first introducing it and motivating it in the context of current technological or scientific advancement, then providing practical applications that students can experiment with, ideally to reproduce a known result in the specific field. A tentative list of topics for this year - scientific machine learning - physics-informed neural networks - theoretical computer science - animal-like behavior in robots - representation learning for health - natural language processing - 3D computer vision - visual text analytics - human-centered AI. The last weeks of the course will be reserved for the implementation of the final project. The students will be assigned group projects in one of the presented areas, based on their preferences. The outcomes will be made into a scientific poster and students will be asked to present the projects to the other groups in a joint poster session. | |||||||||||||||||||||
| Prerequisites / Notice | Participants should have basic knowledge about machine learning and statistics (e.g. Introduction to Machine Learning course or equivalent) and programming. | |||||||||||||||||||||
Competencies |
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